Specifically, we seek to classify images from the CIFAR-100 dataset [1]}. We first generate embeddings from a pretrained gMLP-S model [2]} on \(224 \times 224\)  images from the ImageNet-1k dataset [3]} with \(16 \times 16\)  patches (\(s=196\) , \(d=256\) ). We then finetune the single convex head to classify images from CIFAR-100, while leaving the pre-trained backbone fixed.
